feature standar nb

利用c语言编写，编译成动态链接库而成的matlab整数规划工具箱,包含c语言源程序，(use of language, compiled DLL from the Matlab Integer Programming Toolbox contains language source c,)

利用Abaqus子程序UAMP实现PID控制。(PID control is implemented using the Abaqus subroutine UAMP.)

说明：  应用基本粒子群算法求解复杂连续函数优化问题(Application of Basic Particle Swarm Optimization to Complex Continuous Function Optimization)

说明：  最速下降法就是梯度下降法,可以用于求解最小二乘问题(线性和非线性都可以)。可以说是求解机器算法的最古老、最经典的模型，另一种常用的方法是最小二乘法。(The steepest descent method is the gradient descent method, which can be used to solve the least squares problem (both linear and non-linear). It can be said that it is the oldest and most classical model for solving machine algorithm. Another commonly used method is the least square method.)

ABAQUS扩展子程序开发，用于计算模拟(ABAQUS extended subroutine application)

说明：  编程观察无穷级数 1/n的收敛性。 采用 IEEE 单精度浮点数，观察当 n 为何值时求和结果不再变化，将它与理论分析的结论进 行比较 (注：在 MATLAB 中可用 single 命令将变量转成单精度浮点数)。(Programming observation of convergence of infinite series 1/n)

Fundamentals of matrix computations 矩阵运算的基础(Fundamentals of matrix computations)

内点法求解凸二次规划 COPL_QP是一个可用的凸二次规划实验的解。这个软件包试图解决线性约束凸二次规划。源代码用C语言编写，附带用户指南（postscript文件）和问题实例(Interior point method for solving convex quadratic programming)

说明：  matlab 传感器异步融合仿真程序，目前File Exchange中一共有超过35000个文件，这两天我对这三万多份源代码和模型进行了统计整理，将其中下载量最高的前1000个文件信息及链接，收录到了微信小程序中。(Matlab sensor asynchronous fusion simulation program At present, there are more than 35,000 files in File Exchange. In the past two days, I have compiled and compiled these more than 30,000 source codes and models, and included the information and links of the top 1,000 files with the highest download volume in the WeChat applet.)

支持向量机和BP神经网络都可以用来做非线性回归拟合，但它们的原理是不相同的，支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。大量仿真证实，支持向量机的泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。本源码可以用于线性回归、非线性回归、非线性函数拟合、数据建模、预测、分类等多种应用场合(Support vector machines and BP neural network can be used for non-linear regression fit, but they is not the same principle, support vector machine based on structural risk minimization theory, generally considered its generalization ability than the neural network of strong . Inherent defect- a large number of simulation confirmed that generalization ability of support vector machine is stronger than the BP network, but also to avoid neural network training results unstable. The source code can be used for linear regression, nonlinear regression, nonlinear function fitting a variety of applications, data modeling, forecasting, classification)